Part Whole Hierarchy

Part-whole hierarchies represent the organization of information into nested levels, from individual components to larger systems. Current research focuses on developing algorithms and models, including hierarchical clustering, neural networks (especially transformers and state space models), and genetic sampling techniques, to automatically generate and utilize these hierarchies in diverse applications. This work is significant for improving the efficiency and interpretability of machine learning models, facilitating knowledge discovery in scientific literature, and enabling more robust and adaptable systems in robotics and other fields. The ability to effectively represent and reason with part-whole hierarchies is crucial for advancing artificial intelligence and its applications across various scientific disciplines.